In the last century, molecular crystals functioned predominantly as a means for determining
the molecular structures via X-ray diffraction, albeit as the century came to a close the …

The subject of crystal engineering started in the 1970s with the study of topochemical
reactions in the solid state. A broad chemical definition of crystal engineering was published …

As materials researchers increasingly embrace machine-learning (ML) methods, it is natural
to wonder what lessons can be learned from other fields undergoing similar developments …

We provide an introduction to Gaussian process regression (GPR) machine-learning
methods in computational materials science and chemistry. The focus of the present review …

Metal–organic frameworks (MOFs) are useful synthetic materials that are built by the
programmed assembly of metal nodes and organic linkers. The success of MOFs results …

One of the most exciting tools that have entered the material science toolbox in recent years
is machine learning. This collection of statistical methods has already proved to be capable …

The so-called D4 model is presented for the accurate computation of London dispersion
interactions in density functional theory approximations (DFT-D4) and generally for atomistic …

Artificial intelligence and specifically machine learning applications are nowadays used in a
variety of scientific applications and cutting-edge technologies, where they have a …

Progress in the discovery of new materials has been accelerated by the development of
reliable quantum-mechanical approaches to crystal structure prediction. The properties of a …

While a complete understanding of organic semiconductor (OSC) design principles remains
elusive, computational methods─ ranging from techniques based in classical and quantum …